Visualizing and Comparing LIS Output¶
LIS Output Primer¶
LIS writes model state variables to disk at a frequency selected by the user (e.g., 6-hourly, daily, monthly). The LIS output we will be exploring was originally generated as daily NetCDF files, meaning one NetCDF was written per simulated day. We have converted these NetCDF files into a Zarr store for improved performance in the cloud.
Import Libraries¶
# interface to Amazon S3 filesystem
import s3fs
# interact with n-d arrays
import numpy as np
import xarray as xr
# interact with tabular data (incl. spatial)
import pandas as pd
import geopandas as gpd
# interactive plots
import holoviews as hv
import geoviews as gv
import hvplot.pandas
import hvplot.xarray
# used to find nearest grid cell to a given location
from scipy.spatial import distance
# set bokeh as the holoviews plotting backend
hv.extension('bokeh')
Load the LIS Output¶
The xarray library makes working with labelled n-dimensional arrays easy and efficient. If you’re familiar with the pandas library it should feel pretty familiar.
Here we load the LIS output into an xarray.Dataset object:
# create S3 filesystem object
s3 = s3fs.S3FileSystem(anon=False)
# define the name of our S3 bucket
bucket_name = 'eis-dh-hydro'
# define path to store on S3
lis_output_s3_path = f's3://{bucket_name}/SNOWEX-HACKWEEK/DA_SNODAS/SURFACEMODEL/LIS_HIST.d01.zarr/'
# create key-value mapper for S3 object (required to read data stored on S3)
lis_output_mapper = s3.get_mapper(lis_output_s3_path)
# open the dataset
lis_output_ds = xr.open_zarr(lis_output_mapper, consolidated=True)
# drop some unneeded variables
lis_output_ds = lis_output_ds.drop_vars(['_history', '_eis_source_path'])
Explore the Data¶
Display an interactive widget for inspecting the dataset by running a cell containing the variable name. Expand the dropdown menus and click on the document and database icons to inspect the variables and attributes.
lis_output_ds
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, east_west: 361, north_south: 215, time: 730)
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables: (12/26)
Albedo_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
CanopInt_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ECanop_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ESoil_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
GPP_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
LAI_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
... ...
Swnet_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TVeg_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TWS_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TotalPrecip_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lat (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lon (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- east_west: 361
- north_south: 215
- time: 730
- time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]')
- Albedo_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - CanopInt_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - ECanop_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - ESoil_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - GPP_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - LAI_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Lwnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - NEE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qh_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qle_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qs_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qsb_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - RadT_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SWE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Snowcover_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(time, SoilMoist_profiles, north_south, east_west)float32dask.array<chunksize=(1, 4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 864.55 MiB 1.18 MiB Shape (730, 4, 215, 361) (1, 4, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Swnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TVeg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TWS_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - lat(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - lon(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Accessing Attributes¶
Dataset attributes (metadata) are accessible via the attrs attribute:
lis_output_ds.attrs
{'DX': 0.10000000149011612,
'DY': 0.10000000149011612,
'MAP_PROJECTION': 'EQUIDISTANT CYLINDRICAL',
'NUM_SOIL_LAYERS': 4,
'SOIL_LAYER_THICKNESSES': [10.0,
30.000001907348633,
60.000003814697266,
100.0],
'SOUTH_WEST_CORNER_LAT': 28.549999237060547,
'SOUTH_WEST_CORNER_LON': -113.94999694824219,
'comment': 'website: http://lis.gsfc.nasa.gov/',
'conventions': 'CF-1.6',
'institution': 'NASA GSFC',
'missing_value': -9999.0,
'references': 'Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007',
'source': 'Noah-MP.4.0.1',
'title': 'LIS land surface model output'}
Accessing Variables¶
Variables can be accessed using either dot notation or square bracket notation:
# dot notation
lis_output_ds.SnowDepth_tavg
<xarray.DataArray 'SnowDepth_tavg' (time: 730, north_south: 215, east_west: 361)>
dask.array<open_dataset-3dbd461160b4972901b61d9d102b758bSnowDepth_tavg, shape=(730, 215, 361), dtype=float32, chunksize=(1, 215, 361), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
Dimensions without coordinates: north_south, east_west
Attributes:
long_name: snow depth
standard_name: snow_depth
units: m
vmax: 999999986991104.0
vmin: -999999986991104.0- time: 730
- north_south: 215
- east_west: 361
- dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]')
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
# square bracket notation
lis_output_ds['SnowDepth_tavg']
<xarray.DataArray 'SnowDepth_tavg' (time: 730, north_south: 215, east_west: 361)>
dask.array<open_dataset-3dbd461160b4972901b61d9d102b758bSnowDepth_tavg, shape=(730, 215, 361), dtype=float32, chunksize=(1, 215, 361), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
Dimensions without coordinates: north_south, east_west
Attributes:
long_name: snow depth
standard_name: snow_depth
units: m
vmax: 999999986991104.0
vmin: -999999986991104.0- time: 730
- north_south: 215
- east_west: 361
- dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]')
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Which syntax should I use?¶
While both syntaxes perform the same function, the square-bracket syntax is useful when interacting with a dataset programmatically. For example, we can define a variable varname that stores the name of the variable in the dataset we want to access and then use that with the square-brackets notation:
varname = 'SnowDepth_tavg'
lis_output_ds[varname]
<xarray.DataArray 'SnowDepth_tavg' (time: 730, north_south: 215, east_west: 361)>
dask.array<open_dataset-3dbd461160b4972901b61d9d102b758bSnowDepth_tavg, shape=(730, 215, 361), dtype=float32, chunksize=(1, 215, 361), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
Dimensions without coordinates: north_south, east_west
Attributes:
long_name: snow depth
standard_name: snow_depth
units: m
vmax: 999999986991104.0
vmin: -999999986991104.0- time: 730
- north_south: 215
- east_west: 361
- dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]')
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
The dot notation syntax will not work this way because xarray tries to find a variable in the dataset named varname instead of the value of the varname variable. When xarray can’t find this variable, it throws an error:
# uncomment and run the code below to see the error
# varname = 'SnowDepth_tavg'
# lis_output_ds.varname
Dimensions and Coordinate Variables¶
The dimensions and coordinate variable fields put the “labelled” in “labelled n-dimensional arrays”:
Dimensions: labels for each dimension in the dataset (e.g.,
time)Coordinates: labels for indexing along dimensions (e.g.,
'2019-01-01')
We can use these labels to select, slice, and aggregate the dataset.
Selecting/Subsetting¶
xarray provides two methods for selecting or subsetting along coordinate variables:
index selection:
ds.isel(time=0)value selection
ds.sel(time='2019-01-01')
For example, we can select the first timestep from our dataset using index selection by passing the dimension name as a keyword argument:
# remember: python indexes start at 0
lis_output_ds.isel(time=0)
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, east_west: 361, north_south: 215)
Coordinates:
time datetime64[ns] 2016-10-01
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables: (12/26)
Albedo_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
CanopInt_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
ECanop_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
ESoil_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
GPP_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
LAI_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
... ...
Swnet_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TVeg_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TWS_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TotalPrecip_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
lat (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
lon (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- east_west: 361
- north_south: 215
- time()datetime64[ns]2016-10-01
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array('2016-10-01T00:00:00.000000000', dtype='datetime64[ns]')
- Albedo_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - CanopInt_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - ECanop_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - ESoil_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - GPP_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - LAI_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Lwnet_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - NEE_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qg_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qh_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qle_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qs_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qsb_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - RadT_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SWE_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Snowcover_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(SoilMoist_profiles, north_south, east_west)float32dask.array<chunksize=(4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 1.18 MiB 1.18 MiB Shape (4, 215, 361) (4, 215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Swnet_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TVeg_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TWS_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - lat(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - lon(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Or we can use value selection to select based on the coordinate(s) (think “labels”) of a given dimension:
lis_output_ds.sel(time='2018-01-01')
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, east_west: 361, north_south: 215)
Coordinates:
time datetime64[ns] 2018-01-01
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables: (12/26)
Albedo_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
CanopInt_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
ECanop_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
ESoil_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
GPP_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
LAI_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
... ...
Swnet_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TVeg_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TWS_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
TotalPrecip_tavg (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
lat (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
lon (north_south, east_west) float32 dask.array<chunksize=(215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- east_west: 361
- north_south: 215
- time()datetime64[ns]2018-01-01
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array('2018-01-01T00:00:00.000000000', dtype='datetime64[ns]')
- Albedo_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - CanopInt_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - ECanop_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - ESoil_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - GPP_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - LAI_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Lwnet_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - NEE_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qg_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qh_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qle_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qs_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Qsb_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - RadT_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SWE_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Snowcover_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(SoilMoist_profiles, north_south, east_west)float32dask.array<chunksize=(4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 1.18 MiB 1.18 MiB Shape (4, 215, 361) (4, 215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - Swnet_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TVeg_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TWS_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - lat(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray - lon(north_south, east_west)float32dask.array<chunksize=(215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 303.18 kiB 303.18 kiB Shape (215, 361) (215, 361) Count 732 Tasks 1 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
The .sel() approach also allows the use of shortcuts in some cases. For example, here we select all timesteps in the month of January 2018:
lis_output_ds.sel(time='2018-01')
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, east_west: 361, north_south: 215, time: 31)
Coordinates:
* time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-31
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables: (12/26)
Albedo_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
CanopInt_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ECanop_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ESoil_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
GPP_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
LAI_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
... ...
Swnet_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TVeg_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TWS_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TotalPrecip_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lat (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lon (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- east_west: 361
- north_south: 215
- time: 31
- time(time)datetime64[ns]2018-01-01 ... 2018-01-31
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000', '2018-01-03T00:00:00.000000000', '2018-01-04T00:00:00.000000000', '2018-01-05T00:00:00.000000000', '2018-01-06T00:00:00.000000000', '2018-01-07T00:00:00.000000000', '2018-01-08T00:00:00.000000000', '2018-01-09T00:00:00.000000000', '2018-01-10T00:00:00.000000000', '2018-01-11T00:00:00.000000000', '2018-01-12T00:00:00.000000000', '2018-01-13T00:00:00.000000000', '2018-01-14T00:00:00.000000000', '2018-01-15T00:00:00.000000000', '2018-01-16T00:00:00.000000000', '2018-01-17T00:00:00.000000000', '2018-01-18T00:00:00.000000000', '2018-01-19T00:00:00.000000000', '2018-01-20T00:00:00.000000000', '2018-01-21T00:00:00.000000000', '2018-01-22T00:00:00.000000000', '2018-01-23T00:00:00.000000000', '2018-01-24T00:00:00.000000000', '2018-01-25T00:00:00.000000000', '2018-01-26T00:00:00.000000000', '2018-01-27T00:00:00.000000000', '2018-01-28T00:00:00.000000000', '2018-01-29T00:00:00.000000000', '2018-01-30T00:00:00.000000000', '2018-01-31T00:00:00.000000000'], dtype='datetime64[ns]')
- Albedo_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - CanopInt_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - ECanop_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - ESoil_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - GPP_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - LAI_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Lwnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - NEE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Qg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Qh_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Qle_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Qs_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Qsb_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - RadT_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - SWE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Snowcover_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(time, SoilMoist_profiles, north_south, east_west)float32dask.array<chunksize=(1, 4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 36.71 MiB 1.18 MiB Shape (31, 4, 215, 361) (1, 4, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - Swnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - TVeg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - TWS_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - lat(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray - lon(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 9.18 MiB 303.18 kiB Shape (31, 215, 361) (1, 215, 361) Count 762 Tasks 31 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Select a custom range of dates using Python’s built-in slice() method:
lis_output_ds.sel(time=slice('2018-01-01', '2018-01-15'))
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, east_west: 361, north_south: 215, time: 15)
Coordinates:
* time (time) datetime64[ns] 2018-01-01 2018-01-02 ... 2018-01-15
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables: (12/26)
Albedo_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
CanopInt_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ECanop_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ESoil_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
GPP_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
LAI_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
... ...
Swnet_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TVeg_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TWS_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TotalPrecip_tavg (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lat (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
lon (time, north_south, east_west) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- east_west: 361
- north_south: 215
- time: 15
- time(time)datetime64[ns]2018-01-01 ... 2018-01-15
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000', '2018-01-03T00:00:00.000000000', '2018-01-04T00:00:00.000000000', '2018-01-05T00:00:00.000000000', '2018-01-06T00:00:00.000000000', '2018-01-07T00:00:00.000000000', '2018-01-08T00:00:00.000000000', '2018-01-09T00:00:00.000000000', '2018-01-10T00:00:00.000000000', '2018-01-11T00:00:00.000000000', '2018-01-12T00:00:00.000000000', '2018-01-13T00:00:00.000000000', '2018-01-14T00:00:00.000000000', '2018-01-15T00:00:00.000000000'], dtype='datetime64[ns]')
- Albedo_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - CanopInt_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - ECanop_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - ESoil_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - GPP_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - LAI_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Lwnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - NEE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Qg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Qh_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Qle_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Qs_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Qsb_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - RadT_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - SWE_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Snowcover_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(time, SoilMoist_profiles, north_south, east_west)float32dask.array<chunksize=(1, 4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 17.76 MiB 1.18 MiB Shape (15, 4, 215, 361) (1, 4, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - Swnet_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - TVeg_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - TWS_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - lat(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray - lon(time, north_south, east_west)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 4.44 MiB 303.18 kiB Shape (15, 215, 361) (1, 215, 361) Count 746 Tasks 15 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Latitude and Longitude¶
You may have noticed that latitude (lat) and longitude (lon) are listed as data variables, not coordinate variables. This dataset would be easier to work with if lat and lon were coordinate variables and dimensions. Here we define a helper function that reads the spatial information from the dataset attributes, generates arrays containing the lat and lon values, and appends them to the dataset:
def add_latlon_coords(dataset: xr.Dataset)->xr.Dataset:
"""Adds lat/lon as dimensions and coordinates to an xarray.Dataset object."""
# get attributes from dataset
attrs = dataset.attrs
# get x, y resolutions
dx = round(float(attrs['DX']), 3)
dy = round(float(attrs['DY']), 3)
# get grid cells in x, y dimensions
ew_len = len(dataset['east_west'])
ns_len = len(dataset['north_south'])
# get lower-left lat and lon
ll_lat = round(float(attrs['SOUTH_WEST_CORNER_LAT']), 3)
ll_lon = round(float(attrs['SOUTH_WEST_CORNER_LON']), 3)
# calculate upper-right lat and lon
ur_lat = ll_lat + (dy * ns_len)
ur_lon = ll_lon + (dx * ew_len)
# define the new coordinates
coords = {
# create an arrays containing the lat/lon at each gridcell
'lat': np.linspace(ll_lat, ur_lat, ns_len, dtype=np.float32, endpoint=False),
'lon': np.linspace(ll_lon, ur_lon, ew_len, dtype=np.float32, endpoint=False)
}
lon_attrs = dataset.lon.attrs
lat_attrs = dataset.lat.attrs
# rename the original lat and lon variables
dataset = dataset.rename({'lon':'orig_lon', 'lat':'orig_lat'})
# rename the grid dimensions to lat and lon
dataset = dataset.rename({'north_south': 'lat', 'east_west': 'lon'})
# assign the coords above as coordinates
dataset = dataset.assign_coords(coords)
dataset.lon.attrs = lon_attrs
dataset.lat.attrs = lat_attrs
return dataset
Now that the function is defined, let’s use it to append lat and lon coordinates to the LIS output:
lis_output_ds = add_latlon_coords(lis_output_ds)
Inspect the dataset:
lis_output_ds
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, lat: 215, lon: 361, time: 730)
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
* lat (lat) float32 28.55 28.65 28.75 ... 49.75 49.85 49.95
* lon (lon) float32 -113.9 -113.8 -113.8 ... -78.05 -77.95
Dimensions without coordinates: SoilMoist_profiles
Data variables: (12/26)
Albedo_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
CanopInt_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ECanop_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
ESoil_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
GPP_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
LAI_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
... ...
Swnet_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TVeg_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TWS_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
TotalPrecip_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
orig_lat (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
orig_lon (time, lat, lon) float32 dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- lat: 215
- lon: 361
- time: 730
- time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]') - lat(lat)float3228.55 28.65 28.75 ... 49.85 49.95
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
array([28.55, 28.65, 28.75, ..., 49.75, 49.85, 49.95], dtype=float32)
- lon(lon)float32-113.9 -113.8 ... -78.05 -77.95
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
array([-113.95, -113.85, -113.75, ..., -78.15, -78.05, -77.95], dtype=float32)
- Albedo_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - CanopInt_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - ECanop_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - ESoil_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - GPP_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - LAI_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Lwnet_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - NEE_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qg_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qh_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qle_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qs_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Qsb_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - RadT_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SWE_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Snowcover_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(time, SoilMoist_profiles, lat, lon)float32dask.array<chunksize=(1, 4, 215, 361), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 864.55 MiB 1.18 MiB Shape (730, 4, 215, 361) (1, 4, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - Swnet_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TVeg_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TWS_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - orig_lat(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray - orig_lon(time, lat, lon)float32dask.array<chunksize=(1, 215, 361), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 216.14 MiB 303.18 kiB Shape (730, 215, 361) (1, 215, 361) Count 731 Tasks 730 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Now lat and lon are listed as coordinate variables and have replaced the north_south and east_west dimensions. This will make it easier to spatially subset the dataset!
Basic Spatial Subsetting¶
We can use the slice() function we used above on the lat and lon dimensions to select data between a range of latitudes and longitudes:
lis_output_ds.sel(lat=slice(37, 41), lon=slice(-110, -101))
<xarray.Dataset>
Dimensions: (SoilMoist_profiles: 4, lat: 40, lon: 90, time: 730)
Coordinates:
* time (time) datetime64[ns] 2016-10-01 2016-10-02 ... 2018-09-30
* lat (lat) float32 37.05 37.15 37.25 ... 40.75 40.85 40.95
* lon (lon) float32 -109.9 -109.8 -109.8 ... -101.2 -101.1
Dimensions without coordinates: SoilMoist_profiles
Data variables: (12/26)
Albedo_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
CanopInt_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
ECanop_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
ESoil_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
GPP_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
LAI_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
... ...
Swnet_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
TVeg_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
TWS_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
TotalPrecip_tavg (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
orig_lat (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
orig_lon (time, lat, lon) float32 dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
Attributes: (12/14)
DX: 0.10000000149011612
DY: 0.10000000149011612
MAP_PROJECTION: EQUIDISTANT CYLINDRICAL
NUM_SOIL_LAYERS: 4
SOIL_LAYER_THICKNESSES: [10.0, 30.000001907348633, 60.000003814697266, 1...
SOUTH_WEST_CORNER_LAT: 28.549999237060547
... ...
conventions: CF-1.6
institution: NASA GSFC
missing_value: -9999.0
references: Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
source: Noah-MP.4.0.1
title: LIS land surface model output- SoilMoist_profiles: 4
- lat: 40
- lon: 90
- time: 730
- time(time)datetime64[ns]2016-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2016-10-01T00:00:00.000000000', '2016-10-02T00:00:00.000000000', '2016-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]') - lat(lat)float3237.05 37.15 37.25 ... 40.85 40.95
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
array([37.05, 37.15, 37.25, 37.35, 37.45, 37.55, 37.65, 37.75, 37.85, 37.95, 38.05, 38.15, 38.25, 38.35, 38.45, 38.55, 38.65, 38.75, 38.85, 38.95, 39.05, 39.15, 39.25, 39.35, 39.45, 39.55, 39.65, 39.75, 39.85, 39.95, 40.05, 40.15, 40.25, 40.35, 40.45, 40.55, 40.65, 40.75, 40.85, 40.95], dtype=float32) - lon(lon)float32-109.9 -109.8 ... -101.2 -101.1
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
array([-109.95, -109.85, -109.75, -109.65, -109.55, -109.45, -109.35, -109.25, -109.15, -109.05, -108.95, -108.85, -108.75, -108.65, -108.55, -108.45, -108.35, -108.25, -108.15, -108.05, -107.95, -107.85, -107.75, -107.65, -107.55, -107.45, -107.35, -107.25, -107.15, -107.05, -106.95, -106.85, -106.75, -106.65, -106.55, -106.45, -106.35, -106.25, -106.15, -106.05, -105.95, -105.85, -105.75, -105.65, -105.55, -105.45, -105.35, -105.25, -105.15, -105.05, -104.95, -104.85, -104.75, -104.65, -104.55, -104.45, -104.35, -104.25, -104.15, -104.05, -103.95, -103.85, -103.75, -103.65, -103.55, -103.45, -103.35, -103.25, -103.15, -103.05, -102.95, -102.85, -102.75, -102.65, -102.55, -102.45, -102.35, -102.25, -102.15, -102.05, -101.95, -101.85, -101.75, -101.65, -101.55, -101.45, -101.35, -101.25, -101.15, -101.05], dtype=float32)
- Albedo_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- surface albedo
- standard_name :
- surface_albedo
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - CanopInt_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- total canopy water storage
- standard_name :
- total_canopy_water_storage
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - ECanop_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- interception evaporation
- standard_name :
- interception_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - ESoil_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- bare soil evaporation
- standard_name :
- bare_soil_evaporation
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - GPP_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- gross primary production
- standard_name :
- gross_primary_production
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - LAI_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- leaf area index
- standard_name :
- leaf_area_index
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - LWdown_f_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- surface downward longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vmax :
- 750.0
- vmin :
- 0.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Lwnet_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- net downward longwave radiation
- standard_name :
- surface_net_downward_longwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - NEE_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- net ecosystem exchange
- standard_name :
- net_ecosystem_exchange
- units :
- g m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Qg_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- soil heat flux
- standard_name :
- downward_heat_flux_in_soil
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Qh_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- sensible heat flux
- standard_name :
- surface_upward_sensible_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Qle_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- latent heat flux
- standard_name :
- surface_upward_latent_heat_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Qs_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- surface runoff
- standard_name :
- surface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Qsb_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- subsurface runoff amount
- standard_name :
- subsurface_runoff_amount
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - RadT_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- surface radiative temperature
- standard_name :
- surface_radiative_temperature
- units :
- K
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - SWE_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- snow water equivalent
- standard_name :
- liquid_water_content_of_surface_snow
- units :
- kg m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - SWdown_f_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- surface downward shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vmax :
- 1360.0
- vmin :
- 0.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - SnowDepth_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Snowcover_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- snow cover
- standard_name :
- surface_snow_area_fraction
- units :
- -
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - SoilMoist_tavg(time, SoilMoist_profiles, lat, lon)float32dask.array<chunksize=(1, 4, 40, 90), meta=np.ndarray>
- long_name :
- soil moisture content
- standard_name :
- soil_moisture_content
- units :
- m^3 m-3
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 40.10 MiB 56.25 kiB Shape (730, 4, 40, 90) (1, 4, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - Swnet_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- net downward shortwave radiation
- standard_name :
- surface_net_downward_shortwave_flux
- units :
- W m-2
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - TVeg_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- vegetation transpiration
- standard_name :
- vegetation_transpiration
- units :
- kg m-2 s-1
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - TWS_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- terrestrial water storage
- standard_name :
- terrestrial_water_storage
- units :
- mm
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - TotalPrecip_tavg(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- total precipitation amount
- standard_name :
- total_precipitation_amount
- units :
- kg m-2 s-1
- vmax :
- 0.019999999552965164
- vmin :
- 0.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - orig_lat(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray - orig_lon(time, lat, lon)float32dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
Array Chunk Bytes 10.03 MiB 14.06 kiB Shape (730, 40, 90) (1, 40, 90) Count 1461 Tasks 730 Chunks Type float32 numpy.ndarray
- DX :
- 0.10000000149011612
- DY :
- 0.10000000149011612
- MAP_PROJECTION :
- EQUIDISTANT CYLINDRICAL
- NUM_SOIL_LAYERS :
- 4
- SOIL_LAYER_THICKNESSES :
- [10.0, 30.000001907348633, 60.000003814697266, 100.0]
- SOUTH_WEST_CORNER_LAT :
- 28.549999237060547
- SOUTH_WEST_CORNER_LON :
- -113.94999694824219
- comment :
- website: http://lis.gsfc.nasa.gov/
- conventions :
- CF-1.6
- institution :
- NASA GSFC
- missing_value :
- -9999.0
- references :
- Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
- source :
- Noah-MP.4.0.1
- title :
- LIS land surface model output
Notice how the sizes of the lat and lon dimensions have decreased.
Subset Across Multiple Dimensions¶
Select snow depth for Jan 2017 within a range of lat/lon:
# define a range of dates to select
wy_2018_slice = slice('2017-10-01', '2018-09-30')
lat_slice = slice(37, 41)
lon_slice = slice(-110, -101)
# select the snow depth and subset to wy_2018_slice
snd_CO_wy2018_ds = lis_output_ds['SnowDepth_tavg'].sel(time=wy_2018_slice, lat=lat_slice, lon=lon_slice)
# inspect resulting dataset
snd_CO_wy2018_ds
<xarray.DataArray 'SnowDepth_tavg' (time: 365, lat: 40, lon: 90)>
dask.array<getitem, shape=(365, 40, 90), dtype=float32, chunksize=(1, 40, 90), chunktype=numpy.ndarray>
Coordinates:
* time (time) datetime64[ns] 2017-10-01 2017-10-02 ... 2018-09-30
* lat (lat) float32 37.05 37.15 37.25 37.35 ... 40.65 40.75 40.85 40.95
* lon (lon) float32 -109.9 -109.8 -109.8 -109.7 ... -101.2 -101.2 -101.1
Attributes:
long_name: snow depth
standard_name: snow_depth
units: m
vmax: 999999986991104.0
vmin: -999999986991104.0- time: 365
- lat: 40
- lon: 90
- dask.array<chunksize=(1, 40, 90), meta=np.ndarray>
Array Chunk Bytes 5.01 MiB 14.06 kiB Shape (365, 40, 90) (1, 40, 90) Count 1096 Tasks 365 Chunks Type float32 numpy.ndarray - time(time)datetime64[ns]2017-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2017-10-01T00:00:00.000000000', '2017-10-02T00:00:00.000000000', '2017-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]') - lat(lat)float3237.05 37.15 37.25 ... 40.85 40.95
- long_name :
- latitude
- standard_name :
- latitude
- units :
- degree_north
- vmax :
- 0.0
- vmin :
- 0.0
array([37.05, 37.15, 37.25, 37.35, 37.45, 37.55, 37.65, 37.75, 37.85, 37.95, 38.05, 38.15, 38.25, 38.35, 38.45, 38.55, 38.65, 38.75, 38.85, 38.95, 39.05, 39.15, 39.25, 39.35, 39.45, 39.55, 39.65, 39.75, 39.85, 39.95, 40.05, 40.15, 40.25, 40.35, 40.45, 40.55, 40.65, 40.75, 40.85, 40.95], dtype=float32) - lon(lon)float32-109.9 -109.8 ... -101.2 -101.1
- long_name :
- longitude
- standard_name :
- longitude
- units :
- degree_east
- vmax :
- 0.0
- vmin :
- 0.0
array([-109.95, -109.85, -109.75, -109.65, -109.55, -109.45, -109.35, -109.25, -109.15, -109.05, -108.95, -108.85, -108.75, -108.65, -108.55, -108.45, -108.35, -108.25, -108.15, -108.05, -107.95, -107.85, -107.75, -107.65, -107.55, -107.45, -107.35, -107.25, -107.15, -107.05, -106.95, -106.85, -106.75, -106.65, -106.55, -106.45, -106.35, -106.25, -106.15, -106.05, -105.95, -105.85, -105.75, -105.65, -105.55, -105.45, -105.35, -105.25, -105.15, -105.05, -104.95, -104.85, -104.75, -104.65, -104.55, -104.45, -104.35, -104.25, -104.15, -104.05, -103.95, -103.85, -103.75, -103.65, -103.55, -103.45, -103.35, -103.25, -103.15, -103.05, -102.95, -102.85, -102.75, -102.65, -102.55, -102.45, -102.35, -102.25, -102.15, -102.05, -101.95, -101.85, -101.75, -101.65, -101.55, -101.45, -101.35, -101.25, -101.15, -101.05], dtype=float32)
- long_name :
- snow depth
- standard_name :
- snow_depth
- units :
- m
- vmax :
- 999999986991104.0
- vmin :
- -999999986991104.0
Plotting¶
We’ve imported two plotting libraries:
matplotlib: static plotshvplot: interactive plots
We can make a quick matplotlib-based plot for the subsetted data using the .plot() function supplied by xarray.Dataset objects. For this example, we’ll select one day and plot it:
# simple matplotlilb plot
snd_CO_wy2018_ds.sel(time='2018-01-01').plot()
<matplotlib.collections.QuadMesh at 0x7f5058dc9760>
Similarly we can make an interactive plot using the hvplot accessor and specifying a quadmesh plot type:
# hvplot based map
snd_CO_wy2018_ds.sel(time='2018-01-01').hvplot.quadmesh(geo=True, rasterize=True, project=True,
xlabel='lon', ylabel='lat', cmap='viridis',
tiles='EsriImagery')
Pan, zoom, and scroll around the map. Hover over the LIS data to see the data values.
If we try to plot more than one time-step hvplot will also provide a time-slider we can use to scrub back and forth in time:
snd_CO_wy2018_ds.sel(time='2018-01').hvplot.quadmesh(geo=True, rasterize=True, project=True,
xlabel='lon', ylabel='lat', cmap='viridis',
tiles='EsriImagery')
From here on out we will stick with hvplot for plotting.
Timeseries Plots¶
We can generate a timeseries for a given grid cell by selecting and calling the plot function:
# define point to take timeseries (note: must be present in coordinates of dataset)
ts_lon, ts_lat = (-106.65, 40.25)
# plot timeseries (hvplot knows how to plot based on dataset's dimensionality!)
snd_CO_wy2018_ds.sel(lat=ts_lat, lon=ts_lon).hvplot(title=f'Snow Depth Timeseries @ Lon: {ts_lon}, Lat: {ts_lat}')
In the next section we’ll learn how to create a timeseries over a broader area.
Aggregation¶
We can perform aggregation operations on the dataset such as min(), max(), mean(), and sum() by specifying the dimensions along which to perform the calculation.
For example we can calculate the mean and maximum snow depth at each grid cell over water year 2018 as follows:
# calculate the mean at each grid cell over the time dimension
mean_snd_CO_wy2018_ds = snd_CO_wy2018_ds.mean(dim='time')
max_snd_CO_wy2018_ds = snd_CO_wy2018_ds.max(dim='time')
# plot the mean and max snow depth
mean_snd_CO_wy2018_ds.hvplot.quadmesh(geo=True, rasterize=True, project=True,
xlabel='lon', ylabel='lat', cmap='viridis',
tiles='EsriImagery', title='Mean Snow Depth - WY2018') + \
max_snd_CO_wy2018_ds.hvplot.quadmesh(geo=True, rasterize=True, project=True,
xlabel='lon', ylabel='lat', cmap='viridis',
tiles='EsriImagery', title='Max Snow Depth - WY2018')
Area Average¶
# take area-averaged mean at each timestep
mean_snd_CO_wy2018_ds = snd_CO_wy2018_ds.mean(['lat', 'lon'])
# inspect the dataset
mean_snd_CO_wy2018_ds
<xarray.DataArray 'SnowDepth_tavg' (time: 365)> dask.array<mean_agg-aggregate, shape=(365,), dtype=float32, chunksize=(1,), chunktype=numpy.ndarray> Coordinates: * time (time) datetime64[ns] 2017-10-01 2017-10-02 ... 2018-09-30
- time: 365
- dask.array<chunksize=(1,), meta=np.ndarray>
Array Chunk Bytes 1.43 kiB 4 B Shape (365,) (1,) Count 1826 Tasks 365 Chunks Type float32 numpy.ndarray - time(time)datetime64[ns]2017-10-01 ... 2018-09-30
- begin_date :
- 20161001
- begin_time :
- 000000
- long_name :
- time
- time_increment :
- 86400
array(['2017-10-01T00:00:00.000000000', '2017-10-02T00:00:00.000000000', '2017-10-03T00:00:00.000000000', ..., '2018-09-28T00:00:00.000000000', '2018-09-29T00:00:00.000000000', '2018-09-30T00:00:00.000000000'], dtype='datetime64[ns]')
# plot timeseries (hvplot knows how to plot based on dataset's dimensionality!)
mean_snd_CO_wy2018_ds.hvplot()
Comparing LIS Output¶
Now that we’re familiar with the LIS output, let’s compare it to other datasets.
First, we’re going to define a helper function to get the coordinates of the grid cell nearest to a given lat/lon pair:
LIS (raster) vs. SNODAS (raster)¶
Draft of LIS vs SNODAS comparison.
# load SNODAS dataset
#snodas depth
key = "SNOWEX-HACKWEEK/SNODAS/snodas_snowdepth_20161001_20200930.zarr"
snodas_depth_ds = xr.open_zarr(s3.get_mapper(f"{bucket_name}/{key}"), consolidated=True)
# apply scale factor (0.001 per SNODAS user guide)
snodas_depth_ds = snodas_depth_ds * 0.001
#snodas swe
# key = "SNOWEX-HACKWEEK/SNODAS/snodas_swe_20161001_20200930.zarr"
# snodas_swe_ds = xr.open_zarr(s3.get_mapper(f"{bucket_name}/{key}"), consolidated=True)
Define helper function to get the (lon, lat) of the nearest grid cell to the given pt:
def nearest_grid(ds, pt):
"""
Returns the nearest lon and lat to pt in a given Dataset (ds).
pt : input point, tuple (longitude, latitude)
output:
lon, lat
"""
if all(coord in list(ds.coords) for coord in ['lat', 'lon']):
df_loc = ds[['lon', 'lat']].to_dataframe().reset_index()
else:
df_loc = ds[['orig_lon', 'orig_lat']].isel(time=0).to_dataframe().reset_index()
loc_valid = df_loc.dropna()
pts = loc_valid[['lon', 'lat']].to_numpy()
idx = distance.cdist([pt], pts).argmin()
return loc_valid['lon'].iloc[idx], loc_valid['lat'].iloc[idx]
# SNODAS is at finer resolution than LIS, reinterpolate to LIS output? Has performance penalty...
# snodas_depth_ds.interp_like(lis_output_ds)
# get lon, lat of snodas grid cell nearest to the LIS coordinates we used earlier
snodas_ts_lon, snodas_ts_lat = nearest_grid(snodas_depth_ds, (ts_lon, ts_lat))
# define a date range to plot (shorter = quicker for demo)
start_date, end_date = ('2018-01-01', '2018-03-01')
plot_daterange = slice(start_date, end_date)
# plot LIS vs SNODAS snow depth
(snodas_depth_ds.sel(lon=snodas_ts_lon, lat=snodas_ts_lat, time=plot_daterange).hvplot(ylabel='Snow Depth (m)', label='SNODAS') * \
lis_output_ds['SnowDepth_tavg'].sel(lat=ts_lat, lon=ts_lon, time=plot_daterange).hvplot(label='LIS')).opts(title=f'Snow Depth @ Lon: {ts_lon}, Lat: {ts_lat}', legend_position='right')
LIS (raster) vs. SNOTEL (point)¶
Exercise¶
See the following example for how to build an interactive widget for creating comparison time series of LIS, SNODAS, and SNOTEL, all together in one plot.
